13 Intelligent Access Control with Facial Recognition | Python Programming on Hardware for Beginner

Meimei Jul 13.2023

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Have you ever experienced the frustration of forgetting your keys and being unable to enter your home, or the need to return to double-check if you locked the door after leaving?

Nowadays, facial recognition technology is widely used in various scenarios such as facial scanning for station access, facial payment, campus security management, and facial attendance. If our home door locks could also be opened by recognizing our faces, it would eliminate the inconvenience of losing or forgetting keys.

In this lesson, let's combine the concept of "UNIHIKER" and a camera to design an intelligent access control system that can recognize the owner's face.

Task Objectives

Using a USB camera, we can display the real-time environmental footage. When a recognized face is detected, the corresponding identification (ID) marker will be displayed on the screen, and a servo motor will be controlled to simulate the opening of the door.

Knowledge points

1. Familiarize yourself with servo motors.

2. Learn how to control servo motor rotation using the Pinpong library.

3. Understand facial recognition.

4. Learn how to perform facial recognition using the OpenCV library.

5. Learn how to draw text and rectangles on images using the OpenCV library.

6. Learn how to convert image formats using the PIL library's Image module.

7. Learn file handling methods using the os library.

Software Preparation：Mind+ Programming Softwarex1

Knowledge background

1. What is a servo motor?

A servo motor is an actuator that can control the rotation of an object to a specific position (angle). There are two common types of servo motors: 180° and 360°. In this case, we are using a 180° servo motor.

2. Controlling Servo Rotation with the angle() Method in the Pinpong Library's Servo Class

The angle() method in the Servo class of the Pinpong library allows us to control the rotation of a servo motor to a specific position. Before using this method, we need to import the Pinpong library, initialize the board, and instantiate the Servo class to create an object.

CODE

from pinpong.board import Board, Pin, Servo  # Import the Pinpong library

Board("UNIHIKER").begin()  # Initialize and select the board type, if not specified, it will be automatically recognized

s1 = Servo(Pin(Pin.P23))  # Initialize the servo pin by passing it to the Servo class
s1.angle(10)  # Control the servo to rotate to the initial position of 10 degrees (closed door)

In this case, the Servo module is similar to the Board module, both of which are modules under the pinpong.board package. "P23" refers to the pin to which the servo is connected on the board. "Pin(Pin.P23)" creates a pin object, which is then passed into the Servo class for instantiation, resulting in the creation of a servo object. "10" refers to the 10° position.

3. What is Facial Recognition?

Facial recognition is a biometric technology that involves identifying individuals based on their facial features. It utilizes cameras or webcams to capture images or video streams containing faces and automatically detects and tracks faces within the images. This technology then performs a series of related techniques to recognize and identify the detected faces. Facial recognition is also commonly referred to as face recognition or facial identification.

In simple terms, facial recognition is the process of analyzing and identifying a person's face image to determine their identity. It addresses the question of "whose face is this?"

Facial recognition generally involves the following four steps:

1) Face detection: This is the process of locating the regions of faces in an image. (i.e., determining whether it is a face or not)

2) Face preprocessing: Adjusting the face image to make it appear clearer and more suitable for analysis.

3) Face data collection and learning: Collecting the detected faces that have undergone preprocessing and then learning and memorizing them.

4) Face recognition: Searching among the collected and learned faces to find the one that is most similar to the face being identified.

Among them, the first three steps lay the foundation for the final fourth step of facial recognition. When designing a program for facial recognition, we first need to collect a set of face images from the detected and preprocessed faces. Then, we train these image sets to create a facial model. Finally, during the recognition phase, we capture real-time image data and input it into the generated facial model to obtain a facial result, as shown in the diagram below.

4. What is LBPH Facial Recognition?

Facial detection is the first step in facial recognition. During facial detection, we can utilize cascaded classifiers. Similarly, during facial recognition, we can employ various methods.

OpenCV provides three facial recognition methods: LBPH (Local Binary Patterns Histograms), Eigenfaces, and Fisherfaces. In this lesson, we are using the LBPH method. We will train a facial model and detect real-time faces using the LBPH recognizer provided by OpenCV. Therefore, in this project, we will divide the process into three steps: "Face Data Collection," "Facial Model Training," and "Real-time Facial Recognition."

LBPH (Local Binary Patterns Histograms) is a facial recognition algorithm used to identify faces. The basic idea of the facial recognition method based on LBPH is as follows: first, each pixel is considered as the center, and the relationship between its grayscale value and the surrounding pixels is determined. The pixel is then encoded into a binary pattern, resulting in an LBP (Local Binary Patterns) encoded image of the entire image. Next, the LBP image is divided into regions, and the LBP histogram is obtained for each region. Finally, the LBP histograms of different facial images are compared to achieve facial recognition. The advantage of this method is that it is not affected by lighting conditions, scaling, rotation, or translation.

Here, we only need a basic understanding of LBPH.

5. Using the LBPH Recognizer in the OpenCV Library

(1) Creating the LBPH Recognizer

In OpenCV, you can create an instance of the LBPH recognizer using the function "cv2.face.LBPHFaceRecognizer_create()".

CODE

recognizer = cv2.face.LBPHFaceRecognizer_create()  # Create an instance of the LBPH recognizer model

In this case, "recognizer" is a variable used to store the generated instance of the recognizer model.

(2) Training the LBPH Recognizer Model using the "train()" Method of the Recognizer Object

After generating the LBPH recognizer, you can train the model using the "train()" method. During training, you need to provide the facial image samples and their corresponding ID numbers to the empty model file.

CODE

recognizer.train(faces, np.array(Ids))  # Pass the face samples list and ID samples list to the empty LBPH recognizer model to get a complete face model

In this case, "faces" is a list that stores the facial images used for training, and "np.array(Ids)" refers to the ID labels corresponding to the facial images.

(3) Saving the LBPH Recognizer Model using the "save()" Method of the Recognizer Object

After generating the LBPH recognizer, you can use the "save()" method to save the specified model to a specified path.

CODE

img_src = '/root/image/project14'  # Define the save path (Specify to a fixed location in Mind+)
recognizer.save(img_src+'/model.yml')  # Save the generated model

In this case, "img_src" refers to the specified path for storing the model, and "model.yml" refers to the filename of the saved model file.

(4) Reading the LBPH Recognizer Model using the "read()" Method of the Recognizer Object

After generating the LBPH recognizer, you can use the "read()" method to read the model.

CODE

recognizer.read(img_src + '/model.yml')  # Read the trained face model from the specified path

In this case, "img_src" refers to the path where the model is located, and "model.yml" refers to the filename of the model being read.

(5) Predicting Facial Images using the "predict()" Method of the LBPH Recognizer Object

After reading the facial model, you can use the "predict()" method to recognize and predict facial images.

CODE

img_id, confidence = recognizer.predict(gray[y:y + h, x:x + w])  # Predict on the grayscale image within the specified rectangular region (used for face presentation)

In this case, "gray[y:y + h, x:x + w]" refers to the grayscale image of the facial region used for prediction, "img_id" refers to the returned label of the recognition result, and "confidence" refers to the confidence score returned. The confidence score is used to measure the distance between the recognition result and the original model. A score of 0 indicates a perfect match, and typically, values below 50 are considered acceptable. If the value is greater than 80, it is considered a significant difference.

6. Drawing Text on Images using the "putText()" Function in the OpenCV Library

The "putText()" function in the OpenCV library allows us to draw text on images. To use this function, we need to first import the library and set the desired font type.

CODE

import cv2  # Import the OpenCV library
font = cv2.FONT_HERSHEY_SIMPLEX  # Set the font type to normal-sized sans-serif
cv2.putText(img, 'shooting', (10, 50), font, 0.6, (0, 255, 0), 2)  # Display the text "shooting" on the image, indicating that the face is being captured

In this case, "cv2.FONT_HERSHEY_SIMPLEX" is the chosen font type, stored in the variable "font". "img" refers to the image on which the text will be displayed. "shooting" represents the specific text content to be displayed. "(10, 50)" indicates the position where the text will be drawn, with the bottom left corner of the text as the starting point. "0.6" corresponds to the size of the text. "(0, 255, 0)" represents the color of the text, in this case, green, using RGB values. "2" corresponds to the thickness of the line used for drawing the text.

7. Drawing Rectangles on Images using the "rectangle()" Function in the OpenCV Library

The "rectangle()" function in the OpenCV library allows us to draw rectangular boxes on images to highlight the detected faces.

CODE

cv2.putText(img, 'Done, Please quit', (10, 50), font, 0.6, (0, 255, 0), 2)  # Display the text "Done, Please quit" to indicate completion

In this case, "img" refers to the original image on which the rectangle will be drawn. "(x, y)" corresponds to the top-left coordinates of the rectangle, while "(x + w, y + h)" refers to the bottom-right coordinates of the rectangle. "(0, 255, 0)" represents the color of the rectangle's border, specified using RGB values, with green being used in this case. "2" indicates the thickness of the rectangle's border.

8. Saving Images using the "imwrite()" Function in the OpenCV Library

The "imwrite()" function in the OpenCV library can be used to save a specified image to a specified path.

CODE

lena = cv2.imread("lena.bmp")  # Read the image "lena.bmp"
r = cv2.imwrite("result.bmp", lena)  # Save it as "result.bmp" image

In this case, "lena.bmp" is an image located in the current directory. After reading and creating a copy of this image, it will be saved in the current directory with the name "result.bmp".

9. Image Processing with the PIL Library's Image Module

(1) Opening an Image with the "open()" Function

The "open()" function in the Image module of the PIL library can be used to open an image located at a specified path.

CODE

image = Image.open(image_path)

In this case, "image_path" refers to the path of the image.

(2) Converting the Mode of an Image with the "convert()" Function

The "convert()" function in the Image module of the PIL library can be used to convert the mode of an image.

For color images, regardless of their image format (PNG, BMP, or JPG), when opened using the "open()" function in the Image module of PIL, the returned image object's mode will be "RGB". On the other hand, for grayscale images, regardless of their image format, when opened, the mode will be "L". PIL supports 9 different image modes, with "L" representing grayscale images where each pixel is represented by 8 bits, with 0 representing black and 255 representing white. Other modes represent different color spaces.

CODE

image = Image.open(image_path).convert('L')# Convert the color image to grayscale

In this case, "image_path" refers to the path of the image.

Hands-on practice

Task Description 1: Collecting Facial Images

In this task, we will capture 3 different faces using a camera. Each face will be captured with 50 images and labeled accordingly, so that later during recognition, the corresponding face labels can be displayed.

1. Hardware setup

STEP1：Connect the UNIHIKER to the computer via a USB cable.

STEP2：Connect the USB camera to the UNIHIKER.

2. program coding

STEP 1: Creating and Saving Project Files

Launch Mind+ and save the project as "013 Intelligent Access Control with Facial Recognition".

STEP 2：Creating and Saving Python Files

Create a Python program file named "main1.py" and double-click to open it.

STEP 3：Programming

(1) Importing Required Libraries

In this task, we will need to import the OpenCV library to access the camera and display the video stream. We will also need to import the os library to create a folder for saving the captured images.

CODE

import cv2  # Import the OpenCV library
import os  # Import the os library

(2) Defining the Path and Folder for Image Saving

In this task, we will be capturing facial images and saving them. Therefore, it is necessary to define the path where the images will be saved and create an empty folder to store the images later.

CODE

img_src = '/root/image/project14'  # Define the image path (Specify to a fixed location in Mind+)
os.system('mkdir -p ' + img_src + '/new/')  # Create a folder named "new" in the specified path (The folder needs to be created first before storing the images)
# img_src = os.getcwd()  # Specify the current working directory if running directly

(3) Setting up the Camera

In order to display the real-time camera feed on the UNIHIKER screen, we need to perform four steps. First, we need to open the camera. Then, we set a buffer of 1 frame for the video stream to avoid any lag or high delay. After that, we create a window, and finally, we set the window to fullscreen mode to display the complete video feed on the window.

CODE

cap = cv2.VideoCapture(0)  # Open and initialize the camera
cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)  # Set the buffer size to 1 frame to reduce latency
cv2.namedWindow('frame', cv2.WND_PROP_FULLSCREEN)  # Create a window named 'frame' with the default property of being able to go fullscreen
cv2.setWindowProperty('frame', cv2.WND_PROP_FULLSCREEN, cv2.WINDOW_FULLSCREEN)  # Set the 'frame' window to fullscreen

(4) Setting the Font Type

In order to display some prompt text on the screen during facial image capture, it is necessary to define the font type beforehand.

CODE

font = cv2.FONT_HERSHEY_SIMPLEX  # Set the font type to a normal-sized sans-serif font

(5) Loading the Face Detection Classifier

To detect faces and capture images, we need to use the appropriate classifier tool. Here, we load the classifier in advance.

CODE

detector = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')  # Load the face detection classifier

(6) Defining the Initial Image Sample Count

To keep track of the number of images captured later on, we define the initial image sample count as 0.

CODE

sampleNum = 0  # Initialize the sample number to 0

(7) Labeling the Captured Facial Images

During the facial image capture process, in order to differentiate between different faces, we need to assign an ID number to each face. Here, we achieve this by inputting numerical values through the terminal before capturing the images.

CODE

ID = input('enter your id: ')  # Enter the ID number for the face image data

(8) Facial Image Capture

Next, we need to capture facial images using the webcam. This process can be divided into four steps. Firstly, we display the real-time video stream from the camera on the UNIHIKER screen in proportion. Then, we detect and retrieve facial data. After that, we set the desired number of detected faces. Once we have collected 50 images, we display a completion message on the screen. Otherwise, we show a prompt indicating that the images are being captured and save them into the previously defined folder. Finally, we set the program to exit when the "b" key is pressed.

CODE

while True:
    ret, img = cap.read()  # Read the image frame by frame
    # img = cv2.flip(img, 1)  # Mirror the image (horizontally flip the img image)
    if ret:  # If an image is successfully read
        h, w, c = img.shape  # Record the shape of the image, which includes the height, width, and channels
        w1 = h * 240 // 320
        x1 = (w - w1) // 2
        img = img[:, x1:x1 + w1]  # Crop the image
        img = cv2.resize(img, (240, 320))  # Resize the image to match the dimensions of the PinPong board
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)  # Convert the image to grayscale
        faces = detector.detectMultiScale(gray, 1.3, 5)  # Detect and obtain face recognition data
        for (x, y, w, h) in faces:  # If a face is detected
            cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)  # Draw a rectangle box around the face
            sampleNum = sampleNum + 1  # Increment the sample number
            if sampleNum <= 50:  # If the number of samples is less than or equal to 50
                cv2.putText(img, 'shooting', (10, 50), font, 0.6, (0, 255, 0), 2)  # Display the text "shooting" on the image, indicating that the face is being captured
                # Save the cropped face image with a name format of sampleNum.UserID.jpg
                cv2.imwrite(img_src + '/new/' + str(sampleNum) + '.' + str(ID) + ".jpg", gray[y:y + h, x:x + w])
            else:  # If the number of images exceeds 50
                cv2.putText(img, 'Done, Please quit', (10, 50), font, 0.6, (0, 255, 0), 2)  # Display the text "Done, Please quit" to indicate completion
        cv2.imshow('frame', img)  # Display the image on the 'frame' window

        key = cv2.waitKey(1)  # Delay each frame by 1ms, delay cannot be 0, otherwise the result will be a static frame
        if key & 0xFF == ord('b'):  # Press 'b' to exit
            break

(9) Closing the Camera and Windows

After pressing the "b" key to exit, we release the camera resources and close all image windows.

CODE

cap.release()  # Release the camera
cv2.destroyAllWindows()  # Close all windows

Tips：The complete example program is as follows:

CODE

'''
Run the program, enter the ID in the terminal and press Enter. The screen will display the camera image. Adjust the position until a green box appears, indicating that the photo is being taken. When "done" is displayed, the process is complete.
The captured images will be saved in the "/root/image/project14/new" folder. This code captures 50 images, but you can modify the code parameters to capture more images.
'''

import cv2  # Import the OpenCV library
import os  # Import the os library

img_src = '/root/image/project14'  # Define the image path (Specify to a fixed location in Mind+)
os.system('mkdir -p ' + img_src + '/new/')  # Create a folder named "new" in the specified path (The folder needs to be created first before storing the images)
# img_src = os.getcwd()  # Specify the current working directory if running directly

cap = cv2.VideoCapture(0)  # Open and initialize the camera
cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)  # Set the buffer size to 1 frame to reduce latency
cv2.namedWindow('frame', cv2.WND_PROP_FULLSCREEN)  # Create a window named 'frame' with the default property of being able to go fullscreen
cv2.setWindowProperty('frame', cv2.WND_PROP_FULLSCREEN, cv2.WINDOW_FULLSCREEN)  # Set the 'frame' window to fullscreen

font = cv2.FONT_HERSHEY_SIMPLEX  # Set the font type to a normal-sized sans-serif font
detector = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')  # Load the face detection classifier
sampleNum = 0  # Initialize the sample number to 0
ID = input('enter your id: ')  # Enter the ID number for the face image data

while True:
    ret, img = cap.read()  # Read the image frame by frame
    # img = cv2.flip(img, 1)  # Mirror the image (horizontally flip the img image)
    if ret:  # If an image is successfully read
        h, w, c = img.shape  # Record the shape of the image, which includes the height, width, and channels
        w1 = h * 240 // 320
        x1 = (w - w1) // 2
        img = img[:, x1:x1 + w1]  # Crop the image
        img = cv2.resize(img, (240, 320))  # Resize the image to match the dimensions of the PinPong board
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)  # Convert the image to grayscale
        faces = detector.detectMultiScale(gray, 1.3, 5)  # Detect and obtain face recognition data
        for (x, y, w, h) in faces:  # If a face is detected
            cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)  # Draw a rectangle box around the face
            sampleNum = sampleNum + 1  # Increment the sample number
            if sampleNum <= 50:  # If the number of samples is less than or equal to 50
                cv2.putText(img, 'shooting', (10, 50), font, 0.6, (0, 255, 0), 2)  # Display the text "shooting" on the image, indicating that the face is being captured
                # Save the cropped face image with a name format of sampleNum.UserID.jpg
                cv2.imwrite(img_src + '/new/' + str(sampleNum) + '.' + str(ID) + ".jpg", gray[y:y + h, x:x + w])
            else:  # If the number of images exceeds 50
                cv2.putText(img, 'Done, Please quit', (10, 50), font, 0.6, (0, 255, 0), 2)  # Display the text "Done, Please quit" to indicate completion
        cv2.imshow('frame', img)  # Display the image on the 'frame' window

        key = cv2.waitKey(1)  # Delay each frame by 1ms, delay cannot be 0, otherwise the result will be a static frame
        if key & 0xFF == ord('b'):  # Press 'b' to exit
            break

cap.release()  # Release the camera
cv2.destroyAllWindows()  # Close all windows

1. Running the Program

STEP1：Remote Connect to the UNIHIKER, Run the Program, and Observe the Results

Running Mind+, you will see a prompt in the terminal asking you to enter the ID number corresponding to the face you want to capture. Here, we input "11" and press Enter to confirm. Note: The ID number can be adjusted here.

Subsequently, the camera automatically starts, and the real-time captured video stream is displayed in full screen on the board. Next, align the camera with a face, and you will notice that the face is highlighted with a green bounding box. Above the bounding box, the text "shooting" is displayed, indicating that we are capturing images of that face. When 50 images are successfully captured, the text "Done, Please quit" is displayed above the face, indicating completion. At this point, you can press the onboard button "b" to exit the program.

STEP2：Multiple Captures

Following the same method as described above, run the program two more times to capture two additional faces. Name the IDs of these faces as "12" and "13" respectively.

Tips: Here, we are capturing face images from a card. Similarly, we can also align the camera towards ourselves and capture real faces.

Task Description 2: Training the Face Model

In the previous task, we captured three different face images. Now, we need to process these images and train them into a face model.

1. program coding

STEP 1: Creating and Saving Project Files

Create a new Python program file named "main2.py" and double-click to open it.

STEP 2：Programming

(1) Importing the numpy and PIL libraries, as well as the Image module

In this task, we will be using the numpy library, the PIL library's Image module, and the os library for image processing. We will also be using the face detection classifier and recognizer from the OpenCV library to create the model file. Therefore, we need to import them.

CODE

import cv2  # Import the OpenCV library
import os  # Import the os library
import numpy as np  # Import the numpy library
from PIL import Image  # Import the Image module from the PIL library

(1) Defining the Path to Save the Model File

In this task, we will be training the captured face images into a face model. Here, we define the location where the model file will be saved in advance.

CODE

img_src = '/root/image/project14'  # Define the save path (Specify to a fixed location in Mind+)

(2) Loading the Face Detection Classifier and LBPH Recognizer

In order to detect faces in the saved face images, we still need to use a classifier. Additionally, to train the images into a model, we need to create an empty model instance in advance. Therefore, here, we pre-load the face detection classifier and LBPH recognizer.

CODE

# Initialize the face detector and recognizer
detector = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')  # Load the face detection classifier
recognizer = cv2.face.LBPHFaceRecognizer_create()  # Create an instance of the LBPH recognizer model (empty)

(3) Defining the Function - Getting Face Sample List and ID List

When training the face images into a model, we need to provide the face image samples and their corresponding ID numbers to the empty model file. Therefore, we need to process the captured images to extract the necessary information. Since this process is complex, we will write it as a function.

The entire process can be divided into four steps. First, we concatenate the path and specific name of the image to represent each face image with a complete path. Second, we create two empty lists to store the face image samples and ID samples, respectively. Next, we iterate through each image, draw a bounding box around the face region, and store it in the face image sample list. We also extract the ID number from the image name and store it in the ID list. Finally, we return the two lists containing the face regions and ID numbers, respectively.

CODE

def get_images_and_labels(path):
    # Concatenate the image path to the specific image name, such as '/root/image/project14/new/50.1.jpg', and store it in the 'image_paths' list
    image_paths = [os.path.join(path, f) for f in os.listdir(path)]
    # Create an empty list for face samples
    face_samples = []
    # Create an empty list for IDs
    ids = []
    for image_path in image_paths:  # Traverse the image path
        # Print the name of each image (image name with path)
        print(image_path)
        # Convert the color image to grayscale
        image = Image.open(image_path).convert('L')
        # Convert the grayscale image format to a Numpy array
        image_np = np.array(image, 'uint8')
        '''To get the ID, we split the image path and retrieve the relevant information'''
        # Split by ".", if the last group is "jpg", then execute the following steps
        if os.path.split(image_path)[-1].split(".")[-1] != 'jpg':
            continue
        # Extract the ID number from the complete path name of the image, which is the ID number we set when capturing the image
        image_id = int(os.path.split(image_path)[-1].split(".")[1])
        # Detect the face in the array format and store the results in 'faces'
        faces = detector.detectMultiScale(image_np)  # Detect faces
        # Crop out the face portion from the array format face image and store them in the face samples list, and store the ID number of the image in the ID samples list
        for (x, y, w, h) in faces:
            face_samples.append(image_np[y:y + h, x:x + w])
            ids.append(image_id)
    return face_samples, ids  # Return the face samples list and ID samples list

(4) Getting the Face Sample List and ID List

Here, we call the previously defined function and pass in the image path and name to get the face sample list and ID list.

CODE

faces, Ids = get_images_and_labels(img_src+'/new/')  # Pass in the path and the folder name of the image to get the face samples list and ID samples list

(5) Training the Model

After obtaining the face sample and ID sample lists, we proceed to train the model using them.

CODE

recognizer.train(faces, np.array(Ids))  # Pass the face samples list and ID samples list to the empty LBPH recognizer model to get a complete face model

(6) Saving the Model

Finally, after training the facial model with the collected images, we save it for future use.

CODE

recognizer.save(img_src+'/model.yml')  # Save the generated model
print("generate model done")  # Print "Model has been generated"

Tips：The complete example program is as follows:

CODE

# Train the Captured Face Images and Generate the model.yml (Located at the same level as the 'new' folder)
import cv2  # Import the OpenCV library
import os  # Import the os library
import numpy as np  # Import the numpy library
from PIL import Image  # Import the Image module from the PIL library

img_src = '/root/image/project14'  # Define the save path (Specify to a fixed location in Mind+)

# Initialize the face detector and recognizer
detector = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')  # Load the face detection classifier
recognizer = cv2.face.LBPHFaceRecognizer_create()  # Create an instance of the LBPH recognizer model (empty)

'''
Traverse the image path, import images and IDs, and add them to the list
'''
def get_images_and_labels(path):
    # Concatenate the image path to the specific image name, such as '/root/image/project14/new/50.1.jpg', and store it in the 'image_paths' list
    image_paths = [os.path.join(path, f) for f in os.listdir(path)]
    # Create an empty list for face samples
    face_samples = []
    # Create an empty list for IDs
    ids = []
    for image_path in image_paths:  # Traverse the image path
        # Print the name of each image (image name with path)
        print(image_path)
        # Convert the color image to grayscale
        image = Image.open(image_path).convert('L')
        # Convert the grayscale image format to a Numpy array
        image_np = np.array(image, 'uint8')
        '''To get the ID, we split the image path and retrieve the relevant information'''
        # Split by ".", if the last group is "jpg", then execute the following steps
        if os.path.split(image_path)[-1].split(".")[-1] != 'jpg':
            continue
        # Extract the ID number from the complete path name of the image, which is the ID number we set when capturing the image
        image_id = int(os.path.split(image_path)[-1].split(".")[1])
        # Detect the face in the array format and store the results in 'faces'
        faces = detector.detectMultiScale(image_np)  # Detect faces
        # Crop out the face portion from the array format face image and store them in the face samples list, and store the ID number of the image in the ID samples list
        for (x, y, w, h) in faces:
            face_samples.append(image_np[y:y + h, x:x + w])
            ids.append(image_id)
    return face_samples, ids  # Return the face samples list and ID samples list

# Train the LBPH recognizer model with the face samples and ID samples, and output the corresponding face model (.yml format file)
faces, Ids = get_images_and_labels(img_src+'/new/')  # Pass in the path and the folder name of the image to get the face samples list and ID samples list
recognizer.train(faces, np.array(Ids))  # Pass the face samples list and ID samples list to the empty LBPH recognizer model to get a complete face model
recognizer.save(img_src+'/model.yml')  # Save the generated model
print("generate model done")  # Print "Model has been generated"

2. Running the Program

STEP1：Remote Connect to the UNIHIKER, Run the Program, and Observe the Results

When running Mind+, you can see a series of image file names displayed in the terminal. At the end, it prints "generate model done," indicating that the model files for the three facial images mentioned above have been generated.

Additionally, after reconnecting the UNIHIKER board, you can also find the model file in the "Image" folder.

Tips: If you encounter a missing "cv2.face.LBPHFaceRecognizer_create" library file error during runtime, you can refer to Appendix 2 for offline installation of the missing library.

Task Description 3: Real-time Face Recognition

In the previous task, we trained a model using the collected facial photos. Now, we will utilize this model to perform real-time face detection and recognition. When a recognized face matches one of the collected faces, we will control the servo motor to rotate to the 170° position to open the door. After 5 seconds, the servo motor will automatically return to the 10° position to close the door.

1. Hardware setup

STEP1：Please connect the servo motor to the P23 pin.

STEP2：Connect the USB camera to the UNIHIKER.

2. program coding

STEP 1：Creating and Saving Python Files

Create a Python program file named "main3.py" and double-click to open it.

STEP 2：Programming

(1) Importing Required Libraries

In this task, we will need to import the OpenCV library to access the camera and display the video stream, the PinPong library to control the servo motor movement, and the time library to set delays. Let's import them first.

CODE

import cv2  # Import the OpenCV library
from pinpong.board import Board, Pin, Servo  # Import the Pinpong library
import time  # Import the time library

(2) Initializing the UNIHIKER and controlling the servo motor to the initial position of 10°.

CODE

Board("UNIHIKER").begin()  # Initialize and select the board type, if not specified, it will be automatically recognized

s1 = Servo(Pin(Pin.P23))  # Initialize the servo pin by passing it to the Servo class
s1.angle(10)  # Control the servo to rotate to the initial position of 10 degrees (closed door)
time.sleep(1)

(3) Defining the image path.

CODE

img_src = '/root/image/project14'  # Define the save path (Specify to a fixed location in Mind+)

(4) Setting up the camera and font type.

CODE

cap = cv2.VideoCapture(0)  # Open the camera with index 0 and initialize
cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)  # Set the buffer to 1 frame to reduce latency
cv2.namedWindow('frame', cv2.WND_PROP_FULLSCREEN)  # Create a window with the name 'frame', and set its property to fullscreen
cv2.setWindowProperty('frame', cv2.WND_PROP_FULLSCREEN, cv2.WINDOW_FULLSCREEN)  # Set the 'frame' window to fullscreen
font = cv2.FONT_HERSHEY_SIMPLEX  # Set the font type to normal-sized sans-serif

(5) Loading the face detection classifier and LBPH recognizer.

CODE

detector = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')  # Load the face detection classifier
recognizer = cv2.face.LBPHFaceRecognizer_create()  # Create an instance of the LBPH recognizer model

(6) Reading the face model file.

In this task, we will utilize the trained face model mentioned above to perform real-time face detection and recognition. Therefore, we need to read the model file first.

CODE

recognizer.read(img_src + '/model.yml')  # Read the trained face model from the specified path

(7) Real-time face recognition.

During the face detection and recognition process, we also need to adjust the camera's display so that it can be properly shown on the screen of the UniHiker. After that, we will add a functionality that, when a recognized face is detected, it will draw a rectangle around the face, display the text "Welcome Home" along with the ID number of the face, and control the servo motor to rotate to 170 degrees to simulate the opening of the door. After 5 seconds, the servo motor will return to its initial position at 10 degrees. On the other hand, when an unrecognized face is detected, it will display "unknown" and keep the servo motor at the 10-degree position to simulate the closing of the door.

Tips: Due to the processing time required for the UniHiker's CPU to display the green rectangle and text on the screen, it may be slower than the camera's frame rate. Therefore, not every frame after detecting a face will show the green rectangle and ID text. To address this, we can define a "count" variable to keep track of each frame from the camera, and only display the green rectangle and text on the third frame where a face is detected.

CODE

count = 0  # Define a count flag

while True:
    ret, img = cap.read()  # Read the image frame by frame
    # img = cv2.flip(img, 1)  # Mirror the image (horizontally flip the img image)
    if ret:  # If an image is read successfully
        h, w, c = img.shape  # Record the shape of the image, including height, width, and channels
        w1 = h * 240 // 320
        x1 = (w - w1) // 2
        img = img[:, x1:x1 + w1]  # Crop the image
        img = cv2.resize(img, (240, 320))  # Resize the image to match the UNIHIKER display
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)  # Convert the image to grayscale
        faces = detector.detectMultiScale(gray, 1.2, 5)  # Detect and obtain face recognition data
        for (x, y, w, h) in faces:
            cv2.rectangle(img, (x - 50, y - 50), (x + w + 50, y + h + 50), (0, 255, 0), 2)  # Draw a rectangular box around the face
            img_id, confidence = recognizer.predict(gray[y:y + h, x:x + w])  # Predict on the grayscale image within the specified rectangular region (used for face presentation)
            if confidence < 80:  # If confidence < 80, it means a trained face is detected
                cv2.putText(img, "Welcome Home", (x, y), font, 0.6, (0, 255, 0), 2)  # Add the text "Welcome Home" to the specified position on the image
                cv2.putText(img, str(img_id), (x, y + h), font, 0.6, (0, 255, 0), 2)  # Add the corresponding face ID label to the specified position on the image
                if count == 2:  # On the third frame
                    s1.angle(170)  # Control the servo to rotate to the position of 170 degrees (open the door)
                    print("The door is open")
                    time.sleep(5)  # Wait for five seconds
                    s1.angle(10)  # Control the servo to rotate to the position of 10 degrees (close the door)
                    print("The door is closed")
                    count = 0  # Reset the count
                else:
                    s1.angle(10)
                    count = count + 1  # Increment the frame count
            else:  # If an unknown face is detected
                img_id = "unknown"
                cv2.putText(img, str(img_id), (x, y + h), font, 0.6, (0, 255, 0), 2)  # Add the text "unknown" to the specified position on the image
        cv2.imshow('frame', img)  # Display the image
        key = cv2.waitKey(1)  # Delay each frame by 1ms, delay cannot be 0, otherwise the result will be a static frame
        if key & 0xFF == ord('b'):  # Press 'b' to exit
            break

(8) Closing the camera and window.

CODE

cap.release()  # Release the camera
cv2.destroyAllWindows()  # Close all windows

Tips：The complete example program is as follows:

CODE

'''
Run this program to predict faces using the trained face model. When a trained face is detected, display the ID number and rotate the servo to 170° to open the door. When an unfamiliar face is detected, display "unknown". The more images collected, the higher the recognition accuracy.
'''
import cv2  # Import the OpenCV library
from pinpong.board import Board, Pin, Servo  # Import the Pinpong library
import time  # Import the time library

Board("UNIHIKER").begin()  # Initialize and select the board type, if not specified, it will be automatically recognized

s1 = Servo(Pin(Pin.P23))  # Initialize the servo pin by passing it to the Servo class
s1.angle(10)  # Control the servo to rotate to the initial position of 10 degrees (closed door)
time.sleep(1)

img_src = '/root/image/project14'  # Define the save path (Specify to a fixed location in Mind+)

cap = cv2.VideoCapture(0)  # Open the camera with index 0 and initialize
cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)  # Set the buffer to 1 frame to reduce latency
cv2.namedWindow('frame', cv2.WND_PROP_FULLSCREEN)  # Create a window with the name 'frame', and set its property to fullscreen
cv2.setWindowProperty('frame', cv2.WND_PROP_FULLSCREEN, cv2.WINDOW_FULLSCREEN)  # Set the 'frame' window to fullscreen
font = cv2.FONT_HERSHEY_SIMPLEX  # Set the font type to normal-sized sans-serif

'''Initialize the face detector and recognizer, and use the previously trained .yml file to recognize faces'''
detector = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')  # Load the face detection classifier
recognizer = cv2.face.LBPHFaceRecognizer_create()  # Create an instance of the LBPH recognizer model
recognizer.read(img_src + '/model.yml')  # Read the trained face model from the specified path

count = 0  # Define a count flag

while True:
    ret, img = cap.read()  # Read the image frame by frame
    # img = cv2.flip(img, 1)  # Mirror the image (horizontally flip the img image)
    if ret:  # If an image is read successfully
        h, w, c = img.shape  # Record the shape of the image, including height, width, and channels
        w1 = h * 240 // 320
        x1 = (w - w1) // 2
        img = img[:, x1:x1 + w1]  # Crop the image
        img = cv2.resize(img, (240, 320))  # Resize the image to match the UNIHIKER display
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)  # Convert the image to grayscale
        faces = detector.detectMultiScale(gray, 1.2, 5)  # Detect and obtain face recognition data
        for (x, y, w, h) in faces:
            cv2.rectangle(img, (x - 50, y - 50), (x + w + 50, y + h + 50), (0, 255, 0), 2)  # Draw a rectangular box around the face
            img_id, confidence = recognizer.predict(gray[y:y + h, x:x + w])  # Predict on the grayscale image within the specified rectangular region (used for face presentation)
            if confidence < 80:  # If confidence < 80, it means a trained face is detected
                cv2.putText(img, "Welcome Home", (x, y), font, 0.6, (0, 255, 0), 2)  # Add the text "Welcome Home" to the specified position on the image
                cv2.putText(img, str(img_id), (x, y + h), font, 0.6, (0, 255, 0), 2)  # Add the corresponding face ID label to the specified position on the image
                if count == 2:  # On the third frame
                    s1.angle(170)  # Control the servo to rotate to the position of 170 degrees (open the door)
                    print("The door is open")
                    time.sleep(5)  # Wait for five seconds
                    s1.angle(10)  # Control the servo to rotate to the position of 10 degrees (close the door)
                    print("The door is closed")
                    count = 0  # Reset the count
                else:
                    s1.angle(10)
                    count = count + 1  # Increment the frame count
            else:  # If an unknown face is detected
                img_id = "unknown"
                cv2.putText(img, str(img_id), (x, y + h), font, 0.6, (0, 255, 0), 2)  # Add the text "unknown" to the specified position on the image
        cv2.imshow('frame', img)  # Display the image
        key = cv2.waitKey(1)  # Delay each frame by 1ms, delay cannot be 0, otherwise the result will be a static frame
        if key & 0xFF == ord('b'):  # Press 'b' to exit
            break

cap.release()  # Release the camera
cv2.destroyAllWindows()  # Close all windows

3. Running the Program

STEP1：Remote Connect to the UNIHIKER, Run the Program, and Observe the Results

Observing the UniHiker, once the camera display is shown, we can align it with the faces on the 3 captured cards. We will notice that each face on the image is surrounded by a rectangle, displaying the corresponding ID number and the text "Welcome Home". At this point, the image remains static, and the servo motor rotates from its original position of 10 degrees to 170 degrees. After 5 seconds, it returns to the initial position of 10 degrees. Afterwards, if we align it with an unrecognized face, the text "unknown" will be displayed on the face, while the servo motor remains at the initial position of 10 degrees.

Tips 1: The more images we capture, the higher the accuracy of the recognition. You can adjust the recognition rate by changing the threshold value "80" in the program.

Tips 2：When detecting familiar faces, it is advisable to maintain a consistent environment with the one during image capture. This can improve the recognition rate.

Challenge Yourself

Think about it, what are some areas that you think are worth further improving for the current access control system?

Appendix

Operation Instructions:

STEP 1: Upgrade Mind+ to the latest version (V1.7.2 RC3.0 or above).

STEP 2: Download the "3-1.py" file and the "opencv_contrib_python-4.5.5.64-cp36-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl" file from the link provided in Appendix 1.

STEP 3: Install the library files.

License

All Rights

Reserved

Meimei Jan 15.2024

1389 M-point

61 Makelogs